Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations2111
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory280.5 KiB
Average record size in memory136.1 B

Variable types

Categorical15
Numeric10

Alerts

Dataset has 9 (0.4%) duplicate rowsDuplicates
Age is highly overall correlated with MTRANS_Public_TransportationHigh correlation
CAEC_Frequently is highly overall correlated with CAEC_SometimesHigh correlation
CAEC_Sometimes is highly overall correlated with CAEC_FrequentlyHigh correlation
CALC_Sometimes is highly overall correlated with CALC_noHigh correlation
CALC_no is highly overall correlated with CALC_SometimesHigh correlation
Gender is highly overall correlated with Height and 2 other fieldsHigh correlation
Height is highly overall correlated with Gender and 1 other fieldsHigh correlation
MTRANS_Public_Transportation is highly overall correlated with AgeHigh correlation
NObeyesdad is highly overall correlated with Gender and 1 other fieldsHigh correlation
Weight is highly overall correlated with family_history_with_overweightHigh correlation
family_history_with_overweight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
height is highly overall correlated with Gender and 1 other fieldsHigh correlation
SMOKE is highly imbalanced (85.4%) Imbalance
SCC is highly imbalanced (73.3%) Imbalance
CAEC_no is highly imbalanced (83.6%) Imbalance
CALC_Frequently is highly imbalanced (79.0%) Imbalance
MTRANS_Bike is highly imbalanced (96.8%) Imbalance
MTRANS_Motorbike is highly imbalanced (95.3%) Imbalance
MTRANS_Walking is highly imbalanced (82.3%) Imbalance
FCVC has 33 (1.6%) zeros Zeros
NCP has 439 (20.8%) zeros Zeros
CH2O has 211 (10.0%) zeros Zeros
FAF has 411 (19.5%) zeros Zeros
TUE has 557 (26.4%) zeros Zeros
NObeyesdad has 272 (12.9%) zeros Zeros

Reproduction

Analysis started2024-11-04 22:22:04.418285
Analysis finished2024-11-04 22:22:14.634502
Duration10.22 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
1
1068 
0
1043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Length

2024-11-04T23:22:14.703195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:14.800616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring characters

ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Age
Real number (ℝ)

High correlation 

Distinct1323
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50832636
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:14.924771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23815151
Q10.34480363
median0.47567298
Q30.60688218
95-th percentile0.98914633
Maximum1
Range1
Interquartile range (IQR)0.26207855

Descriptive statistics

Standard deviation0.22141362
Coefficient of variation (CV)0.43557375
Kurtosis-0.31592179
Mean0.50832636
Median Absolute Deviation (MAD)0.1312092
Skewness0.67305466
Sum1073.0769
Variance0.04902399
MonotonicityNot monotonic
2024-11-04T23:22:15.047471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.244068375 128
 
6.1%
0.6068821765 101
 
4.8%
1 96
 
4.5%
0.3954345777 96
 
4.5%
0.4852727061 89
 
4.2%
0.2970280421 59
 
2.8%
0.3474032595 48
 
2.3%
0.4413304512 39
 
1.8%
0.1882446639 30
 
1.4%
0.5274208907 18
 
0.9%
Other values (1313) 1407
66.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.06663519354 1
 
< 0.1%
0.1292293363 9
0.4%
0.1348763892 1
 
< 0.1%
0.1370513325 1
 
< 0.1%
0.1396828279 1
 
< 0.1%
0.1411944688 1
 
< 0.1%
0.1437381878 1
 
< 0.1%
0.1455247486 1
 
< 0.1%
0.147700726 2
 
0.1%
ValueCountFrequency (%)
1 96
4.5%
0.9987788113 1
 
< 0.1%
0.9982713976 1
 
< 0.1%
0.9970224751 1
 
< 0.1%
0.9966742309 1
 
< 0.1%
0.9965137516 1
 
< 0.1%
0.9943930892 1
 
< 0.1%
0.9904863119 1
 
< 0.1%
0.9895402348 1
 
< 0.1%
0.9894333456 1
 
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct1574
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4783251
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:15.183832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18680724
Q10.34210126
median0.47608902
Q30.60526076
95-th percentile0.76022503
Maximum1
Range1
Interquartile range (IQR)0.2631595

Descriptive statistics

Standard deviation0.1773214
Coefficient of variation (CV)0.37071314
Kurtosis-0.56442017
Mean0.4783251
Median Absolute Deviation (MAD)0.13260035
Skewness-0.013312415
Sum1009.7443
Variance0.031442879
MonotonicityNot monotonic
2024-11-04T23:22:15.299022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4751406416 60
 
2.8%
0.3801125133 50
 
2.4%
0.285084385 43
 
2.0%
0.57016877 39
 
1.8%
0.3230956363 36
 
1.7%
0.6651968983 28
 
1.3%
0.513151893 19
 
0.9%
0.342101262 17
 
0.8%
0.4181237646 16
 
0.8%
0.627185647 15
 
0.7%
Other values (1564) 1788
84.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01206097005 1
 
< 0.1%
0.057016877 1
 
< 0.1%
0.06021362323 1
 
< 0.1%
0.06325832446 1
 
< 0.1%
0.06934012468 1
 
< 0.1%
0.07489927018 1
 
< 0.1%
0.07876121332 1
 
< 0.1%
0.09229321879 1
 
< 0.1%
0.09502812833 13
0.6%
ValueCountFrequency (%)
1 1
< 0.1%
0.9990554204 1
< 0.1%
0.945351224 1
< 0.1%
0.9364546906 1
< 0.1%
0.9146704425 1
< 0.1%
0.913060666 1
< 0.1%
0.9122700319 2
0.1%
0.8932644063 1
< 0.1%
0.8923958492 1
< 0.1%
0.8910958644 1
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct1525
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50114731
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:15.405455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14490933
Q10.34548335
median0.50465891
Q30.67830765
95-th percentile0.81679959
Maximum1
Range1
Interquartile range (IQR)0.3328243

Descriptive statistics

Standard deviation0.21229198
Coefficient of variation (CV)0.42361192
Kurtosis-0.74620964
Mean0.50114731
Median Absolute Deviation (MAD)0.1640362
Skewness-0.28764097
Sum1057.922
Variance0.045067884
MonotonicityNot monotonic
2024-11-04T23:22:15.534168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4799219787 59
 
2.8%
0.3902937358 43
 
2.0%
0.1652497417 42
 
2.0%
0.4365830715 40
 
1.9%
0.2870366912 37
 
1.8%
0.340622715 26
 
1.2%
0.04919184451 22
 
1.0%
0.5591032223 20
 
0.9%
0.4629163243 19
 
0.9%
0.09506477939 18
 
0.9%
Other values (1515) 1785
84.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.001728971172 1
< 0.1%
0.006288503586 1
< 0.1%
0.01172174726 1
< 0.1%
0.01430490168 1
< 0.1%
0.0167956865 1
< 0.1%
0.02015140291 1
< 0.1%
0.02247000962 1
< 0.1%
0.03674304229 1
< 0.1%
0.03752270091 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9682197361 1
< 0.1%
0.9511227777 1
< 0.1%
0.9498785535 1
< 0.1%
0.9295155607 1
< 0.1%
0.9267809281 1
< 0.1%
0.924057964 1
< 0.1%
0.9211736233 1
< 0.1%
0.917606831 1
< 0.1%
0.9157114514 1
< 0.1%

family_history_with_overweight
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
1
1726 
0
385 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Length

2024-11-04T23:22:15.628554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:15.700250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring characters

ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

FAVC
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
1
1866 
0
245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Length

2024-11-04T23:22:15.771773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:15.854746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring characters

ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

FCVC
Real number (ℝ)

Zeros 

Distinct810
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70952153
Minimum0
Maximum1
Zeros33
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:15.975520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26160725
Q10.5
median0.692751
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.26696329
Coefficient of variation (CV)0.37625819
Kurtosis-0.6375459
Mean0.70952153
Median Absolute Deviation (MAD)0.192751
Skewness-0.43290583
Sum1497.8
Variance0.071269398
MonotonicityNot monotonic
2024-11-04T23:22:16.099515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 652
30.9%
0.5 600
28.4%
0 33
 
1.6%
0.9115895 2
 
0.1%
0.60749 2
 
0.1%
0.897543 2
 
0.1%
0.721268 2
 
0.1%
0.90823 2
 
0.1%
0.9690155 2
 
0.1%
0.977498 2
 
0.1%
Other values (800) 812
38.5%
ValueCountFrequency (%)
0 33
1.6%
0.001783 1
 
< 0.1%
0.002789 1
 
< 0.1%
0.00438 1
 
< 0.1%
0.0155745 1
 
< 0.1%
0.0180795 1
 
< 0.1%
0.018207 1
 
< 0.1%
0.0263495 1
 
< 0.1%
0.026767 1
 
< 0.1%
0.0317245 1
 
< 0.1%
ValueCountFrequency (%)
1 652
30.9%
0.9992205 1
 
< 0.1%
0.9989755 1
 
< 0.1%
0.998762 1
 
< 0.1%
0.9983585 1
 
< 0.1%
0.998093 1
 
< 0.1%
0.9977995 1
 
< 0.1%
0.99724 1
 
< 0.1%
0.9961645 1
 
< 0.1%
0.9961025 1
 
< 0.1%

NCP
Real number (ℝ)

Zeros 

Distinct333
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50337987
Minimum0
Maximum1
Zeros439
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:16.219549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.375
median0.625
Q30.625
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.29371228
Coefficient of variation (CV)0.58348038
Kurtosis-0.48485059
Mean0.50337987
Median Absolute Deviation (MAD)0
Skewness-0.68199628
Sum1062.6349
Variance0.086266901
MonotonicityNot monotonic
2024-11-04T23:22:16.346570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.625 1203
57.0%
0 439
 
20.8%
1 136
 
6.4%
0.4640636859 2
 
0.1%
0.364216794 2
 
0.1%
0.9922324183 2
 
0.1%
0.59173755 1
 
< 0.1%
0.4313899736 1
 
< 0.1%
0.6095378045 1
 
< 0.1%
0.6169559073 1
 
< 0.1%
Other values (323) 323
 
15.3%
ValueCountFrequency (%)
0 439
20.8%
0.007577849462 1
 
< 0.1%
0.01485105274 1
 
< 0.1%
0.01540338341 1
 
< 0.1%
0.02395149109 1
 
< 0.1%
0.04816829669 1
 
< 0.1%
0.05992178383 1
 
< 0.1%
0.06023385078 1
 
< 0.1%
0.0615803128 1
 
< 0.1%
0.06199157899 1
 
< 0.1%
ValueCountFrequency (%)
1 136
6.4%
0.9960668328 1
 
< 0.1%
0.9956726357 1
 
< 0.1%
0.9946310008 1
 
< 0.1%
0.9922324183 2
 
0.1%
0.9914920876 1
 
< 0.1%
0.989238533 1
 
< 0.1%
0.9839269363 1
 
< 0.1%
0.9769280264 1
 
< 0.1%
0.9744678394 1
 
< 0.1%

SMOKE
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2067 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Length

2024-11-04T23:22:16.436027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:16.516096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

CH2O
Real number (ℝ)

Zeros 

Distinct1268
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5040057
Minimum0
Maximum1
Zeros211
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:16.625481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.29240625
median0.5
Q30.73871
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.44630375

Descriptive statistics

Standard deviation0.30647673
Coefficient of variation (CV)0.60808186
Kurtosis-0.87939461
Mean0.5040057
Median Absolute Deviation (MAD)0.226493
Skewness-0.10491164
Sum1063.956
Variance0.093927984
MonotonicityNot monotonic
2024-11-04T23:22:16.745482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 448
 
21.2%
0 211
 
10.0%
1 162
 
7.7%
0.9128145 3
 
0.1%
0.318163 3
 
0.1%
0.5579835 2
 
0.1%
0.587124 2
 
0.1%
0.7650175 2
 
0.1%
0.7250345 2
 
0.1%
0.219981 2
 
0.1%
Other values (1258) 1274
60.4%
ValueCountFrequency (%)
0 211
10.0%
0.0002315 1
 
< 0.1%
0.000268 1
 
< 0.1%
0.000272 1
 
< 0.1%
0.0003475 1
 
< 0.1%
0.0006535 1
 
< 0.1%
0.0009975 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.0015315 1
 
< 0.1%
0.0017815 1
 
< 0.1%
ValueCountFrequency (%)
1 162
7.7%
0.9997475 1
 
< 0.1%
0.9972575 1
 
< 0.1%
0.996724 1
 
< 0.1%
0.9958355 1
 
< 0.1%
0.9946945 1
 
< 0.1%
0.9943855 1
 
< 0.1%
0.993859 1
 
< 0.1%
0.993703 1
 
< 0.1%
0.9921615 1
 
< 0.1%

SCC
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2015 
1
 
96

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Length

2024-11-04T23:22:16.841240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:16.931228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

FAF
Real number (ℝ)

Zeros 

Distinct1190
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3367659
Minimum0
Maximum1
Zeros411
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:17.035521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.041501667
median0.33333333
Q30.55555917
95-th percentile0.89237767
Maximum1
Range1
Interquartile range (IQR)0.5140575

Descriptive statistics

Standard deviation0.28353081
Coefficient of variation (CV)0.84192257
Kurtosis-0.62058776
Mean0.3367659
Median Absolute Deviation (MAD)0.26805233
Skewness0.49848961
Sum710.91281
Variance0.08038972
MonotonicityNot monotonic
2024-11-04T23:22:17.154025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 411
 
19.5%
0.3333333333 234
 
11.1%
0.6666666667 183
 
8.7%
1 75
 
3.6%
0.03672466667 2
 
0.1%
0.553852 2
 
0.1%
0.08178466667 2
 
0.1%
0.355939 2
 
0.1%
0.09601066667 2
 
0.1%
0.4174906667 2
 
0.1%
Other values (1180) 1196
56.7%
ValueCountFrequency (%)
0 411
19.5%
3.2 × 10-51
 
< 0.1%
9.066666667 × 10-51
 
< 0.1%
0.0001513333333 1
 
< 0.1%
0.0003383333333 1
 
< 0.1%
0.000362 1
 
< 0.1%
0.000424 1
 
< 0.1%
0.0004323333333 1
 
< 0.1%
0.0006766666667 1
 
< 0.1%
0.00114 1
 
< 0.1%
ValueCountFrequency (%)
1 75
3.6%
0.9999726667 1
 
< 0.1%
0.9996603333 1
 
< 0.1%
0.9906106667 1
 
< 0.1%
0.979911 1
 
< 0.1%
0.9788503333 1
 
< 0.1%
0.9771756667 1
 
< 0.1%
0.9643073333 2
 
0.1%
0.9639953333 1
 
< 0.1%
0.9637266667 1
 
< 0.1%

TUE
Real number (ℝ)

Zeros 

Distinct1129
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32893296
Minimum0
Maximum1
Zeros557
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:17.263837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.312675
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.30446363
Coefficient of variation (CV)0.92560997
Kurtosis-0.5486604
Mean0.32893296
Median Absolute Deviation (MAD)0.242436
Skewness0.61850241
Sum694.37748
Variance0.092698102
MonotonicityNot monotonic
2024-11-04T23:22:17.380537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 557
26.4%
0.5 292
 
13.8%
1 109
 
5.2%
0.315433 4
 
0.2%
0.5599385 3
 
0.1%
0.0013 3
 
0.1%
0.004627 2
 
0.1%
0.4162 2
 
0.1%
0.682975 2
 
0.1%
0.4142745 2
 
0.1%
Other values (1119) 1135
53.8%
ValueCountFrequency (%)
0 557
26.4%
3.65 × 10-51
 
< 0.1%
0.0001775 1
 
< 0.1%
0.000218 1
 
< 0.1%
0.000548 1
 
< 0.1%
0.000665 1
 
< 0.1%
0.0006685 1
 
< 0.1%
0.000759 1
 
< 0.1%
0.000795 1
 
< 0.1%
0.00082 1
 
< 0.1%
ValueCountFrequency (%)
1 109
5.2%
0.996095 1
 
< 0.1%
0.9953085 1
 
< 0.1%
0.991839 1
 
< 0.1%
0.9904375 1
 
< 0.1%
0.9890215 1
 
< 0.1%
0.986463 1
 
< 0.1%
0.985585 1
 
< 0.1%
0.9847535 1
 
< 0.1%
0.9836295 1
 
< 0.1%

NObeyesdad
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0156324
Minimum0
Maximum6
Zeros272
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-11-04T23:22:17.474280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9520902
Coefficient of variation (CV)0.64732365
Kurtosis-1.1906523
Mean3.0156324
Median Absolute Deviation (MAD)2
Skewness0.0067544491
Sum6366
Variance3.810656
MonotonicityNot monotonic
2024-11-04T23:22:17.560614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 351
16.6%
4 324
15.3%
3 297
14.1%
5 290
13.7%
6 290
13.7%
1 287
13.6%
0 272
12.9%
ValueCountFrequency (%)
0 272
12.9%
1 287
13.6%
2 351
16.6%
3 297
14.1%
4 324
15.3%
5 290
13.7%
6 290
13.7%
ValueCountFrequency (%)
6 290
13.7%
5 290
13.7%
4 324
15.3%
3 297
14.1%
2 351
16.6%
1 287
13.6%
0 272
12.9%

CAEC_Frequently
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
1869 
1
242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1869
88.5%
1 242
 
11.5%

Length

2024-11-04T23:22:17.818636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:17.887813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1869
88.5%
1 242
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 1869
88.5%
1 242
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1869
88.5%
1 242
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1869
88.5%
1 242
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1869
88.5%
1 242
 
11.5%

CAEC_Sometimes
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
1
1765 
0
346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1765
83.6%
0 346
 
16.4%

Length

2024-11-04T23:22:17.966680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:18.033643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1765
83.6%
0 346
 
16.4%

Most occurring characters

ValueCountFrequency (%)
1 1765
83.6%
0 346
 
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1765
83.6%
0 346
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1765
83.6%
0 346
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1765
83.6%
0 346
 
16.4%

CAEC_no
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2060 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2060
97.6%
1 51
 
2.4%

Length

2024-11-04T23:22:18.109744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:18.183508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2060
97.6%
1 51
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 2060
97.6%
1 51
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2060
97.6%
1 51
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2060
97.6%
1 51
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2060
97.6%
1 51
 
2.4%

CALC_Frequently
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2041 
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2041
96.7%
1 70
 
3.3%

Length

2024-11-04T23:22:18.257537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:18.323460image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2041
96.7%
1 70
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 2041
96.7%
1 70
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2041
96.7%
1 70
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2041
96.7%
1 70
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2041
96.7%
1 70
 
3.3%

CALC_Sometimes
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
1
1401 
0
710 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1401
66.4%
0 710
33.6%

Length

2024-11-04T23:22:18.415078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:18.514404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1401
66.4%
0 710
33.6%

Most occurring characters

ValueCountFrequency (%)
1 1401
66.4%
0 710
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1401
66.4%
0 710
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1401
66.4%
0 710
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1401
66.4%
0 710
33.6%

CALC_no
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
1472 
1
639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1472
69.7%
1 639
30.3%

Length

2024-11-04T23:22:18.632410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:18.718411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1472
69.7%
1 639
30.3%

Most occurring characters

ValueCountFrequency (%)
0 1472
69.7%
1 639
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1472
69.7%
1 639
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1472
69.7%
1 639
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1472
69.7%
1 639
30.3%

MTRANS_Bike
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2104 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2104
99.7%
1 7
 
0.3%

Length

2024-11-04T23:22:18.819754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:18.919781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2104
99.7%
1 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2104
99.7%
1 7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2104
99.7%
1 7
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2104
99.7%
1 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2104
99.7%
1 7
 
0.3%

MTRANS_Motorbike
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2100 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2100
99.5%
1 11
 
0.5%

Length

2024-11-04T23:22:18.995772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:19.096749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2100
99.5%
1 11
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2100
99.5%
1 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2100
99.5%
1 11
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2100
99.5%
1 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2100
99.5%
1 11
 
0.5%

MTRANS_Public_Transportation
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
1
1580 
0
531 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1580
74.8%
0 531
 
25.2%

Length

2024-11-04T23:22:19.224767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:19.345767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1580
74.8%
0 531
 
25.2%

Most occurring characters

ValueCountFrequency (%)
1 1580
74.8%
0 531
 
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1580
74.8%
0 531
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1580
74.8%
0 531
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1580
74.8%
0 531
 
25.2%

MTRANS_Walking
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
2055 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2055
97.3%
1 56
 
2.7%

Length

2024-11-04T23:22:19.436812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T23:22:19.514835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2055
97.3%
1 56
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 2055
97.3%
1 56
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2055
97.3%
1 56
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2055
97.3%
1 56
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2055
97.3%
1 56
 
2.7%

height
Real number (ℝ)

High correlation 

Distinct1574
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9932757
Minimum0.89608802
Maximum1.0919233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2024-11-04T23:22:19.601324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.89608802
5-th percentile0.93542274
Q10.96698385
median0.99343657
Q31.0182927
95-th percentile1.047319
Maximum1.0919233
Range0.19583528
Interquartile range (IQR)0.051308808

Descriptive statistics

Standard deviation0.034584412
Coefficient of variation (CV)0.034818543
Kurtosis-0.56174845
Mean0.9932757
Median Absolute Deviation (MAD)0.025787213
Skewness-0.087120095
Sum2096.805
Variance0.0011960816
MonotonicityNot monotonic
2024-11-04T23:22:19.732329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.993251773 60
 
2.8%
0.97455964 50
 
2.4%
0.955511445 43
 
2.0%
1.011600912 39
 
1.8%
0.9631743178 36
 
1.7%
1.029619417 28
 
1.3%
1.00063188 19
 
0.9%
0.9669838462 17
 
0.8%
0.9820784724 16
 
0.8%
1.022450928 15
 
0.7%
Other values (1564) 1788
84.7%
ValueCountFrequency (%)
0.8960880246 1
 
< 0.1%
0.8986748798 1
 
< 0.1%
0.9082585602 1
 
< 0.1%
0.9089365561 1
 
< 0.1%
0.9095818778 1
 
< 0.1%
0.9108696644 1
 
< 0.1%
0.9120453329 1
 
< 0.1%
0.9128612579 1
 
< 0.1%
0.9157149662 1
 
< 0.1%
0.9162907319 13
0.6%
ValueCountFrequency (%)
1.091923301 1
< 0.1%
1.090466871 1
< 0.1%
1.080925461 1
< 0.1%
1.079336023 1
< 0.1%
1.075433388 1
< 0.1%
1.075144392 1
< 0.1%
1.075002423 2
0.1%
1.071583616 1
< 0.1%
1.071427097 1
< 0.1%
1.071192786 1
< 0.1%

Interactions

2024-11-04T23:22:13.214431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-11-04T23:22:09.153151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:10.139518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:11.099508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:11.769504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-11-04T23:22:07.388161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:08.290568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:09.029806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:10.040514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-11-04T23:22:11.693503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:12.443838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-04T23:22:13.133420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-11-04T23:22:19.856308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AgeCAEC_FrequentlyCAEC_SometimesCAEC_noCALC_FrequentlyCALC_SometimesCALC_noCH2OFAFFAVCFCVCGenderHeightMTRANS_BikeMTRANS_MotorbikeMTRANS_Public_TransportationMTRANS_WalkingNCPNObeyesdadSCCSMOKETUEWeightfamily_history_with_overweightheight
Age1.0000.1830.2380.1650.1060.2420.2440.013-0.2080.1350.0620.217-0.0030.0000.0000.6500.233-0.1160.2840.1830.148-0.2980.3570.303-0.003
CAEC_Frequently0.1831.0000.8110.0470.0580.1240.0990.1900.0810.1770.1390.1200.1910.0000.0630.0630.0310.1670.4890.1080.0290.1570.4760.2660.175
CAEC_Sometimes0.2380.8111.0000.3510.0910.1040.0650.2150.1520.1880.1920.0670.1610.0000.0620.0570.0710.1950.4920.1570.0420.2050.4770.3390.156
CAEC_no0.1650.0470.3511.0000.0000.0450.0490.2060.1380.0120.1330.0560.1690.0000.0000.0470.0000.1270.2550.0580.0000.1080.3220.1840.221
CALC_Frequently0.1060.0580.0910.0001.0000.2560.1170.0820.0740.0480.0430.0240.0550.0000.0000.0820.0000.0000.1400.0500.0910.0910.1350.0000.102
CALC_Sometimes0.2420.1240.1040.0450.2561.0000.9240.1840.2000.1360.1810.0300.1620.0000.0000.0940.0430.2020.3720.0330.0000.2170.3610.0080.175
CALC_no0.2440.0990.0650.0490.1170.9241.0000.1670.1720.1180.1690.0080.1720.0000.0000.0600.0280.2140.3520.0000.0440.2140.3390.0240.174
CH2O0.0130.1900.2150.2060.0820.1840.1671.0000.1560.1950.0660.2380.2250.0250.0360.1130.1250.0700.1060.1310.0750.0230.2260.2330.225
FAF-0.2080.0810.1520.1380.0740.2000.1720.1561.0000.1560.0280.2650.3260.0860.0400.1370.1780.141-0.1260.1000.0680.051-0.0440.1590.326
FAVC0.1350.1770.1880.0120.0480.1360.1180.1950.1561.0000.0880.0600.2140.0660.0130.0160.1740.0000.3280.1860.0400.1710.3050.2050.211
FCVC0.0620.1390.1920.1330.0430.1810.1690.0660.0280.0881.0000.347-0.0560.0000.0000.1600.0850.0880.0140.0940.000-0.0880.2080.121-0.056
Gender0.2170.1200.0670.0560.0240.0300.0080.2380.2650.0600.3471.0000.6190.0440.0320.1580.0290.0990.5560.0980.0350.1310.4300.0990.628
Height-0.0030.1910.1610.1690.0550.1620.1720.2250.3260.214-0.0560.6191.0000.0000.1080.1280.0410.1930.0430.1710.1710.0820.4630.2941.000
MTRANS_Bike0.0000.0000.0000.0000.0000.0000.0000.0250.0860.0660.0000.0440.0001.0000.0000.0870.0000.0000.0650.0000.0000.0000.0000.0000.000
MTRANS_Motorbike0.0000.0630.0620.0000.0000.0000.0000.0360.0400.0130.0000.0320.1080.0001.0000.1150.0000.0000.0800.0230.0000.0560.0320.0360.000
MTRANS_Public_Transportation0.6500.0630.0570.0470.0820.0940.0600.1130.1370.0160.1600.1580.1280.0870.1151.0000.2810.0620.2610.0000.0000.1940.2950.0530.181
MTRANS_Walking0.2330.0310.0710.0000.0000.0430.0280.1250.1780.1740.0850.0290.0410.0000.0000.2811.0000.0000.2130.0360.0000.1610.1140.0590.052
NCP-0.1160.1670.1950.1270.0000.2020.2140.0700.1410.0000.0880.0990.1930.0000.0000.0620.0001.000-0.1500.0000.0000.082-0.0110.1610.193
NObeyesdad0.2840.4890.4920.2550.1400.3720.3520.106-0.1260.3280.0140.5560.0430.0650.0800.2610.213-0.1501.0000.2350.111-0.0570.4040.5400.043
SCC0.1830.1080.1570.0580.0500.0330.0000.1310.1000.1860.0940.0980.1710.0000.0230.0000.0360.0000.2351.0000.0330.1290.2600.1810.164
SMOKE0.1480.0290.0420.0000.0910.0000.0440.0750.0680.0400.0000.0350.1710.0000.0000.0000.0000.0000.1110.0331.0000.0580.0370.0000.175
TUE-0.2980.1570.2050.1080.0910.2170.2140.0230.0510.171-0.0880.1310.0820.0000.0560.1940.1610.082-0.0570.1290.0581.000-0.0500.1880.082
Weight0.3570.4760.4770.3220.1350.3610.3390.226-0.0440.3050.2080.4300.4630.0000.0320.2950.114-0.0110.4040.2600.037-0.0501.0000.6060.463
family_history_with_overweight0.3030.2660.3390.1840.0000.0080.0240.2330.1590.2050.1210.0990.2940.0000.0360.0530.0590.1610.5400.1810.0000.1880.6061.0000.280
height-0.0030.1750.1560.2210.1020.1750.1740.2250.3260.211-0.0560.6281.0000.0000.0000.1810.0520.1930.0430.1640.1750.0820.4630.2801.000

Missing values

2024-11-04T23:22:14.245168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-04T23:22:14.529212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPSMOKECH2OSCCFAFTUENObeyesdadCAEC_FrequentlyCAEC_SometimesCAEC_noCALC_FrequentlyCALC_SometimesCALC_noMTRANS_BikeMTRANS_MotorbikeMTRANS_Public_TransportationMTRANS_Walkingheight
000.3954350.3230960.330238100.50.62500.500.0000000.5101000100100.963174
100.3954350.1330390.240904101.00.62511.011.0000000.0101001000100.924259
210.4852730.6651970.454251100.50.62500.500.6666670.5101010000101.029619
310.6444310.6651970.536301001.00.62500.500.6666670.0501010000011.029619
410.4413300.6271860.557607000.50.00000.500.0000000.0601001000101.022451
510.7156660.3230960.204128010.50.62500.500.0000000.0101001000000.963174
600.4852730.0950280.228865111.00.62500.500.3333330.0101001001000.916291
710.4413300.3611070.204128000.50.62500.501.0000000.0101001000100.970779
810.5274210.6271860.330238111.00.62500.500.3333330.5101010000101.022451
910.4413300.5131520.370858110.50.62500.500.3333330.5101000100101.000632
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211000.5134800.5489510.824719111.00.62500.93175700.3421510.357069401001000101.007533

Duplicate rows

Most frequently occurring

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710.3954350.3230960.390294010.50.00001.000.3333330.0500101000100.96317415
300.3954350.1330390.049192011.00.00000.000.0000000.0010001000100.9242594
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100.2440680.3230960.228865110.50.62500.000.3333330.5110000100100.9631742
200.3954350.1330390.049192001.00.00000.000.0000000.0010001000100.9242592
400.4413300.4561350.340623110.50.62500.500.3333330.5101001000100.9895412
500.5679160.2280680.228865010.50.00000.500.6666670.0101001000100.9439062
610.2440680.5131520.204128110.50.62500.500.0000001.0001001000101.0006322
810.4413300.5511630.436583111.00.62500.000.3333330.0110000100001.0079582